Geographical validation of the Smart Triage Model by age group

Infectious diseases in neonates account for half of the under-five mortality in low- and middle-income countries. Data-driven algorithms such as clinical prediction models can be used to efficiently detect critically ill children in order to optimize care and reduce mortality. Thus far, only a handful of prediction models have been externally validated and are limited to neonatal in-hospital mortality. The aim of this study is to externally validate a previously derived clinical prediction model (Smart Triage) using a combined prospective baseline cohort from Uganda and Kenya with a composite endpoint of hospital admission, mortality, and readmission. We evaluated model discrimination using area under the receiver-operator curve (AUROC) and visualized calibration plots with age subsets (< 30 days, ≤ 2 months, ≤ 6 months, and < 5 years). Due to reduced performance in neonates (< 1 month), we re-estimated the intercept and coefficients and selected new thresholds to maximize sensitivity and specificity. 11595 participants under the age of five (under-5) were included in the analysis. The proportion with an endpoint ranged from 8.9% in all children under-5 (including neonates) to 26% in the neonatal subset alone. The model achieved good discrimination for children under-5 with AUROC of 0.81 (95% CI: 0.79–0.82) but poor discrimination for neonates with AUROC of 0.62 (95% CI: 0.55–0.70). Sensitivity at the low-risk thresholds (CI) were 85% (83%–87%) and 68% (58%–76%) for children under-5 and neonates, respectively. After model revision for neonates, we achieved an AUROC of 0.83 (95% CI: 0.79–0.87) with 13% and 41% as the low- and high-risk thresholds, respectively. The updated Smart Triage performs well in its predictive ability across different age groups and can be incorporated into current triage guidelines at local healthcare facilities. Additional validation of the model is indicated, especially for the neonatal model.


Introduction
Since 1990, overall under-5 mortality has reduced significantly, although this improvement is largely attributed to improved outcomes among non-neonatal populations [1].Low-and middle-income countries (LMICs), particularly in Sub-Saharan Africa and South Asia, continue to contribute disproportionately to childhood deaths globally [1].Amongst the five million children under-5 that died in 2020, 2.4 million were neonates with infectious diseases such as diarrhea, lower respiratory tract infections, meningitis, and malaria being major contributors [2].Sepsis is a dysfunctional inflammatory pathway leading to infection, death, and co-morbidities, and accounts for the majority of emergency and acute care visits in LMICs [3].
Effective triage facilitates early identification of critically ill children and can improve outcomes through case prioritization as most in-hospital deaths in resource-poor settings occur within 24 hours of admission [4,5].The Emergency Triage Assessment and Treatment (ETAT) guidelines have been developed by the World Health Organization (WHO) for the assessment, triage, and initial management of acutely ill children in resource-poor facilities [6].The complexity of ETAT requires clinical knowledge, extensive memorization, and repetitive training, making its implementation a challenge in an environment where patient burden and new-staff turnover are high [7][8][9].An alternate solution is using electronic platforms, with or without clinical prediction models, which uses data-driven algorithms to prioritize care [10,11].One such example is the Smart Triage model, a 9-predictor pediatric triage model that can be embedded into a digital triage platform [12].Despite current progress, these models cannot be widely disseminated due to their lack of generalizability and external validation [13,14,15].
Prediction models are increasingly being used for individualized decision-making and to inform service delivery planning in health care [14].However, these models need to be externally validated before implementation in clinical settings [14,16,17].Whether a model should be re-derived or updated during external validation depends on its performance in the validation cohort, the availability of research resources, and the characteristics of participants in which the model will be applied [15].By keeping the same predictors, updating a model can maintain predictive performance without losing prior information captured in the original model [16].Thus, there is a clear need to optimize existing prediction models for new settings [15].
The aim of this study is to externally validate the Smart Triage model as this can bridge the gap between prediction model development and implementation to ensure the model's reproducibility and generalizability [14].Geographical validation and subsetting of data are two ways to do so.We conducted geographical validation by combining data from three hospital sites in Uganda and two sites in Kenya.We further subset the data by age groups to measure its accuracy and applicability in different age groups in a dataset as certain subgroup(s) can lead to skewed or inaccurate predictions.As age is an important factor in risk prediction, we aim to remove older age groups from the data to measure model accuracy.The neonatal period is recognized as a period associated with the highest clinical risk [18], likely due to differences in inciting infections, disease susceptibility, host response, and underlying physiological reserve [19].To optimize the performance of the risk prediction across ages we hypothesize that patients under one month of age (neonates) have different risks and physiology; therefore, require a different model.We update the model for this age group through a sequence of model-updating procedures [20].Our research addresses the gap in the availability of prediction models for neonates using routine clinical variables that are easy to collect.Our model strives to target patient prioritization and decrease triage wait times for those critically ill by incorporating a combination of adverse health outcomes i.e. mortality, hospital admission and readmission.Due to the limited neonatal prediction model's geographic region and clinical contexts represented in the data, we acknowledge further validation in additional geographic and clinical contexts is needed to ensure accuracy, generalizability, and reproducibility.

Method
Model external validation and updating followed the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) guidelines on developing, validating, or updating a multivariable clinical prediction model [21].

Study population and design
The Smart Triage model was developed based on a prospective baseline cohort study conducted between April 2020 -March 2021 at the outpatient pediatric departments (OPD) of Jinja Regional Referral Hospital (Jinja), a public hospital funded by the Uganda Ministry of Health.It is the largest referral hospital in Eastern Uganda and serves patients residing in Jinja and eight surrounding districts.Its OPD, which functions similarly to an emergency department in high-income countries, evaluates between 20 and 100 patients per day and has an admission rate of approximately 20%.
Data used for validation were prospective baseline cohort studies conducted from March 2021 -April 2022 at Gulu, December 2021 -May 2022 at Ibanda, December 2021 -June 2022 at Kitovu, February-December 2021 at Mbagathi, and March 2021 -December 2022 at Kiambu.Study nurses were recruited and trained to conduct study-specific procedures.They recruited and consented participants in the triage waiting area using a quasi-random sampling method based on time cut-offs, and collected health data.The OPD at Gulu, Ibanda, and Kitovu see approximately 150,000, 19,000, and 19,000 patients annually with an admission rate of 18%, 33%, and 28%, respectively.The two hospitals in Kenya receive approximately 20,000 patients per year with an admission rate of 7% to 10%.

Sampling and eligibility
The full details of study procedures are presented in previous publications [10,12].Briefly, children under 12, 15, or 19 years of age seeking assessment for an acute illness at the pediatric emergency department of all hospital sites between 8:00 am and 5:00 pm were enrolled using time cut off sampling procedures.In addition to parental/caregiver consent, assent was obtained for children above eight years of age at Jinja and Gulu and 13 years of age in Kenya; although this study only uses under-5 data.Children at Ibanda and Kitovu were not individually consented since the program was implemented as a quality improvement program with a waiver of individual consent.Children presenting for elective procedures, scheduled appointments, or treatment of chronic illnesses were not eligible for enrollment.

Data collection and management
Data collection at all sites followed the same procedures that were used to develop the initial model at Jinja [12].Data were collected using password-secured Android tablets and a custom-built mobile application with an encrypted database.The iSpO2 Pulse Oximeter (Masimo, Irvine, CA) with micro-USB connector was connected directly to the tablet to collect pulse oximetry and heart rate, and the SureTemp 692 (Welch Allyn, Skaneateles Falls, NY) thermometer was used to measure core temperature.Data was uploaded directly from the Android tablets to REDCap (Research Electronic Data Capture) [22] and sent to the central study server at the BC Children's Hospital Research Institute and KEMRI Wellcome Trust Research Programme office in Uganda and Kenya, respectively.After each upload, data on the tablets was automatically deleted.Standard operating protocols for data collection and management are available on the Pediatric Sepsis CoLab Dataverse [23].

Primary outcome
The composite endpoint composed of one or more of the following: hospital admission of 24 hours or more determined from hospital records, readmission within 48 hours of enrollment, and mortality including in-hospital or post-discharge.Admission, readmission, and in-hospital or post-discharge mortality status were confirmed by follow-up phone calls to the caregivers 7 days post-study enrollment (for non-admitted patients) or post-discharge.As a secondary analysis, the proportion of children with a composite endpoint was compared using Fisher's exact test between those over six months of age to under-5 and those six months and under.

Smart Triage Model
The multiple logistic model was derived using the bootstrap stepwise regression method and based on clinical validity with nine predictor variables included in the final equation.The model was integrated into a mobile application with a built-in pulse oximetry application (with connected pulse oximeter sensor), providing a smart algorithm that detects disease severity or level of risk in a child presenting to the hospital.The mobile application sends data to an interactive dashboard that provides clinical measurements and triage data to physicians in real-time allowing for rapid identification and assessment of critically ill children [12,24].
The nine predictors included in the model were the square root of age, to attempt to linearize the nonlinear relationship between age and risk, heart rate, temperature, mid-upper arm circumference (MUAC), transformed oxygen saturation (using the concept of virtual shunt [25]), parental concern, difficulty breathing, oedema, and pallor (S1 Appendix).

Statistical analysis
Sample size.Sample size was pre-determined at each site prior to enrollment.In Uganda, it was computed based on the formula N = (nx10)/I where N is the sample size, n is the number of candidate predictor variables, and I is the estimated event rate in the population.The Smart Triage model developed at Jinja has nine predictors and an admission rate of 20%; thus, requiring a minimum sample of 450 participants.In other Ugandan sites, admission rate was used to calculate approximate sample size needed since predictors were already determined.In Kenya, a four-step procedure was implemented in the pmsampsize R package [26] to determine the minimum required sample to perform model validation.An input C-statistic of 0.8, an admission rate of 0.05, a Cox-Snell R-sq of 0.0697 based on 0.05 acceptable difference in apparent and adjusted R-squared, 0.05 margin of error in estimation of intercept, and an event per predictor parameter of 7 were assumed.
Model Validation, calibration, and update.The Smart Triage model equation was applied to a combined dataset comprising of five hospital sites, separated into four age groups: < 1 month, � 2 months, � 6 months, and < 5 years.These age groups were chosen based on the WHO's age classification for special statistics of infant mortality [27].The older age groups were subsequently removed from the data starting with > 6 months, then > 2 months, followed by � 1 month until only neonates remained to assess the model's performance in the younger population.We also performed validation using exclusive age groups: >6m to < 5y, >2m to 6m, and 1m to 2m, and neonates only.The sample size for each age category was still sufficient following the N = (nx10)/I rule.The model was assessed for its overall performance, discrimination, and calibration [14,28,29].The overall performance of the model was assessed using Brier Score, ranging from 0 to 1, with smaller values indicating a better model.Discrimination was assessed using area under the receiver operating curve (AUROC) and visualized with receiver operating characteristic (ROC) curves.AUROC close to 1 indicates good discrimination, while AUROC close to 0.5 indicates an inability to discriminate [28].Calibration was evaluated via calibration plots of predicted versus observed outcome rates with a 45-degree line representing perfect calibration [29].A calibration intercept of 0 and a slope of 1 is considered ideal.The model was updated for neonates only through a series of steps from recalibration to model revision, including the original Jinja cohort.The first step was recalibration-inthe-large to address the difference in baseline risks by re-estimating the model intercept.The next step was logistic recalibration to re-estimate the intercept and slope.Finally, a model revision was performed by re-estimating all regression coefficients using the same set of predictors.Age was not square rooted due to a narrower range.Each step was visually examined with a calibration plot along with AUROC, Observed/Expected ratio, and calibration intercept and slope.An internal validation was performed using a 10-fold cross-validation procedure applied to the entire dataset due to the smaller sample size of neonates.A pooled estimate of AUROC, sensitivity, specificity, and predictive values was computed to quantify predictive accuracy.
Risk stratification.Following our previously reported model development process [12], two new risk thresholds were selected for the new model to divide participants into three triage categories (emergency, priority, and non-urgent).The low-risk threshold was selected at 90% sensitivity to limit misclassification of emergency and priority cases as non-urgent (avoiding false negatives), while the high-risk threshold was selected at 90% specificity to limit misclassification of non-urgent or priority cases as emergency cases (avoiding false positives).A risk classification table was used to examine the accuracy of the updated model in classifying patients.
Missing values were very few and were imputed using median and mode for continuous and categorical variables, respectively.

Participants
A total of 13285 participants were enrolled in the study with 11595 (87%) under-5 included in the analysis and neonates accounted for 404 (3.5%) of the under-5.There was a higher prevalence of males in all the age groups, with a ratio of roughly 53% vs 46% (Table 1).Approximately 9% of the participants under-five were admitted to the hospital and that proportion increased as younger age groups were considered as the denominator, reaching 26% in neonates.There was a statistically significant difference (p-value < 0.0001) in the proportion of participants with a composite endpoint between those over six months of age and those six months and under.For all age groups, more than 90% of those admitted had a minimum length of stay of 24 hours and less than 1% were sent home and readmitted within 48 hours.Pneumonia and neonatal sepsis were the most common reasons for admission in participants under-5.Malaria was more common in older children (140 (13.9%)) and no neonates were admitted with a malaria diagnosis.

Model performance and risk stratification
The overall performance of the Smart Triage model was best when all age groups were included with a Brier score of 0.08.As older age groups were removed, the Brier score increased to 0.18 in neonates.The model achieved good discrimination for all age groups, except neonates, with AUROC values ranging from 0.81 (95% CI: 0.79-0.82)for under-5 to 0.70 (95% CI: 0.65-0.76)for two months and under (Fig 1).The model achieved poor discrimination for neonates with an AUROC of 0.63 (95% CI: 0.56-0.70).The calibration slope also decreased in younger age groups with a value of 0.78 for under-5 to 0.42 for neonates (Fig 2).The assessment of exclusive age groups:>6m to < 5y, >2m to 6m, and 1m to 2m also resulted in good discrimination with AUROC of 0.82, 0.85, and 0.83, respectively, for all age groups except neonates (S1 Fig) .Calibration plots showed a similar phenomenon (S2 Fig) .When comparing model performance between inclusive and exclusive groups, the inclusion of younger participants decreased the model performance.
Table 2 shows the sensitivity and the specificity of the Smart Triage model by triage categories for each age group.The model achieved 85% (95% CI: 83%-87%) sensitivity at the low-risk threshold (non-urgent) and 93% (95% CI: 93%-94%) specificity at the high-risk threshold (emergency) for children under-5.Sensitivity decreased as older cohorts were taken out, while specificity remained relatively unchanged.For neonates, sensitivity was 68% (95% CI: 58%-76%) at the low-risk threshold, and specificity was 96% (95% CI: 94%-98%) at the high-risk threshold.The model identified around 10% of the participants as emergency.When examining mutually exclusive age groups, sensitivity remained high, ranging from 86% to 90% at the low-risk threshold, and specificity ranged from 93% to 98% (S1 Table ).

Model update for neonates
As discrimination, calibration, and sensitivity dropped by 0.19, 0.36, and 17%, respectively, an updated model was developed for neonates.Two new thresholds were chosen at 0.13 and 0.41 for the low and high risk, respectively, based on the desired sensitivity and specificity.Sensitivity at the low-risk threshold was 91% (95% CI: 86%-95%), and specificity at the high-risk threshold was 90% (95% CI: 87%-93%).The new model placed 23% of neonates into the emergency category, and 68% of those had a composite endpoint.

Discussion
This study externally validated the Smart Triage model in all children under five years of age using combined datasets from five hospital sites in Uganda and Kenya and evaluated model performance across different age groups.Neonates had a higher proportion of composite endpoint at 26% compared to 8.9% in those under-5.The model achieved good discrimination with an AUROC of 0.81 in all children under-5.However, its discrimination-the ability to differentiate between those with and without the outcome [30]-decreased each time when older age cohorts were excluded, down to an AUROC of 0.62 for neonates.The agreement between the observed and model's predicted risks of having a composite endpoint (calibration) also deteriorated for those two months and under.Similarly, when comparing model performance using data that included neonates to data that did not, performance was better in the dataset that excluded neonates; therefore, the original Smart Triage model is not suitable for neonates.The model was updated for this age group by re-estimating the baseline risk and the

External validation
External validation showed that the model maintained good discrimination in the new cohort at all sites.However, the model overpredicted the probability of having a composite endpoint due to a lower admission rate among the new cohorts at Gulu and the two sites in Kenya.Differences in the outcome incidence is the most likely cause of miscalibration.When an algorithm is developed in a setting with high disease prevalence, it may systematically overestimate risk when used in settings with lower disease incidence [31].The difference in performance is due to the strength of the association between some predictors and the outcome being substantially different in the new population [32].This was evident when refitting the logistic regression in the new combined under-5 cohort (S2 Table ).Heart rate and oedema were no longer statistically significant predictors because there were fewer percentage of children with oedema at the five sites despite the sample size being ten times larger (S3 Table ).Additionally, the relationship between age and outcome changed from being positively associated to negatively associated in this refitting (S2 Table ).The proportion of outcomes per categorical predictor were less in the external validation data set (S2 Table ).The large variability in predictor values and their effects can degrade overall performance.The ability for the model to distinguish atrisk population and the accuracy of that distinction decreased as older participants were excluded.This is clinically anticipated due to differences in physiology and pathology in the younger age group.Another reason for the decrease in discriminative performance is the age range is smaller in neonates, and age is a predictor in our model that affects model performance [14,33].Calibration in younger age groups is more heterogeneous (Fig 2), which may be magnified by smaller sample sizes, differences in case mix between the development and validation cohorts, and participants being clinically heterogeneous amongst each other.

Model update for neonates
We updated the model for neonates as they differ in disease susceptibly, host response, and underlying physiological reserve.The first 28 days of life is the most vulnerable period and neonates are more likely to be admitted to hospital [18].Low birth weight, congenital malformations, unique infections, and gestational age are factors related to a worse outcome that may not significantly impact older children [34].Factors impacting neonatal outcomes may be different from those one month and older.This was evident in our data as half of the predictors used in the original model to predict admission, readmission, and mortality among children under-5 were no longer statistically significant in the updated model among neonates (S4 Table ).A reason for this is the lack of statistical power due to smaller sample size as a result of subsetting the data.Nonetheless, transformed oxygen saturation (OR 1.05, 95%CI 1.02-1.08)and temperature (OR 1.96, 95%CI 1.45-2.69)are still significant risk factors (S4 Table ).The reduced performance of the model for neonates was anticipated.We updated the model by following a sequence of three updating methods that differed in extensiveness.Adjusting the intercept (baseline risk) showed a modest improvement in calibration.Re-estimation of the intercept and slope continued to improve calibration; however, re-estimating all coefficients of the included predictors was necessary to improve discrimination as it is evident in the improvement of AUROC from 0.66 to 0.83.Previous studies have shown that when the model's discrimination needs to be improved, revision methods are necessary [16,33].Model updating with a new set of predictors may be the optimal approach; however, a trade-off between research resources and model improvement needs to be considered.Revision methods with small adjustments for predictors are preferred when a particular predictor in the original model has a different effect in the new population [16].In our study, we opted to keep the original predictors and update the coefficients.A previous study has demonstrated that retaining the original selected variables where all coefficients are re-estimated using a large dataset improves both discrimination and calibration of the model [35].

Clinical implication
External validation of clinical prediction models is critically important before implementation because models generally perform poorer during external validation [14].It is imperative that prediction models are accurate in order to deliver precise case prioritization and appropriate individualized care recommendations based on illness severity to optimize patient outcomes.Thus far, a review that examined 84032 studies on prediction models concluded that only 5% had been externally validated [14].Furthermore, current neonatal predictive models used in low resources settings are developed mainly for estimating in-hospital mortality [34,36].The outcome of severely ill children is based on more than just in-hospital mortality as admission, readmission, and post-discharge mortality are intricately intertwined in impacting child survival.Other models were limited to predefined populations such as infants born to mothers with severe preeclampsia or premature/low birth weight infants, making these models less generalizable [37,38].One of the models developed at a neonatal intensive care unit in Tanzania [36] identified birthweight, temperature, and respiratory rate to be predictive of in-hospital mortality and we also identified these factors to be important predictors for our composite endpoint.Two other inpatient neonatal mortality prediction models developed in Kenya and Uganda included many of the predictors included in Smart Triage [10,39].Through these consistencies, we have demonstrated that the Smart Triage, a parsimonious triage model, performs well during external validation in a different but related geographical context, making it reproducible.The model is currently implemented at several sites in Uganda and Kenya [40,41] to improve patient prioritization and decrease triage wait times.We have also demonstrated that significantly improved performance can be achieved in a data subset by updating a model based on the same parsimonious variables.At the low-risk threshold, the model is able to exclude low-risk patients given its high negative predictive value.A positive predictive value of 68% (95% CI 61%-76%) was achieved at the high-risk threshold which demonstrates the capability of the model to correctly classify high-risk patients.The positive likelihood ratio indicates a 5.8-fold increase in the odds of needing hospital admission for a patient classified as emergency (Table 3).Furthermore, 91% of participants admitted for at least 24 hours, and 92% of deaths were contained in either the priority or emergency category (Table 3).The parsimony of the Smart Triage model is easy to interpret and understand.This interpretability is particularly important where the results of a model may be used to make life-changing clinical decisions impacting public health outcomes.It requires less computation power and time to train, evaluate, and deploy compared to ETAT, making it practical for real-time applications.It can be easily incorporated into a digital application for timely identification of critically ill children and prompt treatment, improving the efficiency of the public health system and leading to an increase of child survival.

Strengths and limitations
The key limitation of this study is that the validation sites were in adjacent geographical regions.The sites were similar in types of health facilities, but there were differences in disease prevalence and socioeconomic status between facilities that added to the heterogeneity of the Note: NPV = negative predictive value; PPV = positive predictive value, CI = confidence interval. 1percentage based on total number across three categories in the age group.
cohorts.The optimal resemblance between the original and validation cohorts is a trade-off between reproducibility and generalizability.The next steps would be external validation on a different continent or in an urban tertiary care facility.Subsetting our data was a further limitation as subgroups in a dataset are underrepresented, leading to skewed or inaccurate predictions introducing a potential data bias.However, we used our clinical knowledge of agespecific disease processes to stratify our cohort into specific age categories, and with additional cohorts, we were able to stratify subsets by age and improve the model performance with model adjustment in the neonatal age group.A key strength of this study is the use of a large dataset from multiple sites for external validation to offer good statistical power.The use of routinely available clinical data resulted in low rates of missing data.The updated model was internally validated using the bootstrapping technique, which is the most widely recommended technique for internal validation as it allows derivation of the final model from the full derivation sample and does not waste precious information [42].In addition, the revised model used a portion of the original data, preventing overfitting [42].

Conclusion
The Smart Triage model has been externally validated for similar clinical contexts in East Africa.An updated model for neonates is proposed, but will require additional external validation.The model is currently implemented in Uganda and Kenya to rapidly identify critically ill children at the first point of contact using routine clinical data and readily available vital signs.There is demonstrated evidence of improvement in the quality of care and patient outcomes as well as cost-effectiveness [43,44].We believe the model is well adapted to use in resource-poor settings; however, further research is needed to continue to refine the model to increase its reproducibility and generalizability by implementing a range of supervised machine learning methods such as random forest, bagging classifiers, and decision trees to obtain a more accurate model.The major current gap is a lack of platforms to implement and update models in clinical environments.Prediction models can be updated in real-time as clinical data are locally collected, but this needs to be done based on robust and safe implementation frameworks.This will result in more localized clinical practice guidance and care more specific to the targeted population.
Fig 3 shows the sequence of the model update process.Each step resulted in an improvement in calibration (reaching the ideal value of 1 at step two) but limited improvement in discrimination until the final stage of model revision.The final model resulted in an AUROC of 0.83 (95% CI: 0.79-0.87)and calibration intercept and slope of 0 and 1, respectively (Fig 4).New intercept and coefficients for the predictor variables

Fig 1 .
Fig 1. ROC curves by age groups.Participants in each model is not mutually exclusive as the under 5 years old includes those in the younger age group.AUC = area under the curve; CI = confidence interval.https://doi.org/10.1371/journal.pdig.0000311.g001

Fig 2 .
Fig 2. Calibration plots by age groups.Participants in each model is not mutually exclusive as the under 5 years old includes those in the younger age group.https://doi.org/10.1371/journal.pdig.0000311.g002

Fig 3 .
Fig 3. Model update for neonates only.Calibration plots for a sequence of model update.E:O = expected vs. observed probabilities; CITL = calibration-in-the-large index, showing the difference between the average predicted probabilities and the observed event frequencies; AUC = area under the curve.https://doi.org/10.1371/journal.pdig.0000311.g003
Note: age groups are inclusive, i.e. < 5 years includes the younger age groups.1percentage is based on total number included in the study 2 percentage based on total number in the age group.

Table 2 . Summary of risk stratification into the three triage categories and model performance.
Note: NPV = negative predictive value; PPV = positive predictive value.1percentagebased on total number across three categories in the specific group. 2percentage based on total number in the risk category in the specific age group https://doi.org/10.1371/journal.pdig.0000311.t002